code: def sst_get_trainable_data(phrases, word_id_dict, max_input_length):
      texts, labels, mask = [], [], []
  
      for phrase, label in phrases:
          if not phrase.split():
              continue
          phrase_split, mask_split = sst_get_id_input(
              phrase, word_id_dict, max_input_length
          )
          texts.append(phrase_split)
          labels.append(int(label))
          mask.append(mask_split)  # field_input is mask
      labels = np.array(labels, dtype=np.int64)
      texts = np.reshape(texts, [-1, max_input_length]).astype(np.int32)
      mask = np.reshape(mask, [-1, max_input_length]).astype(np.int32)
  
      return SSTDataset(texts, mask, labels)
  
imports: import logging
  import os
  import pickle
  from collections import Counter
  import numpy
  import torch
  from torch.utils import data
label_desc: Use image node_id 10 for calling a global function with example usage: sst_get_trainable_data(phrases, word_id_dict, max_input_length) and returns: SSTDataset